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wwz4l.py
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wwz4l.py
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#!/usr/bin/env python
#import sys
import math
import coffea
import numpy as np
import awkward as ak
import copy
np.seterr(divide='ignore', invalid='ignore', over='ignore')
from coffea import processor
import hist
from hist import axis
from coffea.analysis_tools import PackedSelection
from coffea.lumi_tools import LumiMask
from topcoffea.modules.paths import topcoffea_path
import topcoffea.modules.event_selection as es_tc
import topcoffea.modules.object_selection as os_tc
import topcoffea.modules.corrections as cor_tc
from ewkcoffea.modules.paths import ewkcoffea_path as ewkcoffea_path
import ewkcoffea.modules.selection_wwz as es_ec
import ewkcoffea.modules.objects_wwz as os_ec
import ewkcoffea.modules.corrections as cor_ec
from topcoffea.modules.get_param_from_jsons import GetParam
get_tc_param = GetParam(topcoffea_path("params/params.json"))
get_ec_param = GetParam(ewkcoffea_path("params/params.json"))
import hist.dask as hda
# Small helper function for creating the list of systematics
# Append "Up" and "Down" to all base strings in a given syst list
def append_up_down_to_sys_base(sys_lst_in):
sys_lst_out = []
for s in sys_lst_in:
sys_lst_out.append(f"{s}Up")
sys_lst_out.append(f"{s}Down")
return sys_lst_out
class AnalysisProcessor(processor.ProcessorABC):
def __init__(self, samples, wc_names_lst=[], hist_lst=None, ecut_threshold=None, do_errors=False, do_systematics=False, split_by_lepton_flavor=False, skip_signal_regions=False, skip_control_regions=False, muonSyst='nominal', dtype=np.float32):
self._samples = samples
self._wc_names_lst = wc_names_lst
self._dtype = dtype
# Create the dense axes for the histograms
self._dense_axes_dict = {
"mt2" : axis.Regular(180, 0, 100, name="mt2", label="mt2"),
"met" : axis.Regular(180, 0, 300, name="met", label="met"),
"metphi": axis.Regular(180, -4, 4, name="metphi", label="met phi"),
"ptl4" : axis.Regular(180, 0, 500, name="ptl4", label="ptl4"),
"scalarptsum_lep" : axis.Regular(180, 0, 500, name="scalarptsum_lep", label="S_T"),
"scalarptsum_lepmet" : axis.Regular(180, 0, 600, name="scalarptsum_lepmet", label="S_T + metpt"),
"scalarptsum_lepmetjet" : axis.Regular(180, 0, 1100, name="scalarptsum_lepmetjet", label="S_T + metpt + H_T"),
"mll_01": axis.Regular(180, 0, 200, name="mll_01", label="mll_l0_l1"),
"mllll": axis.Regular(180, 0, 600, name="mllll", label="mllll"),
"l0pt" : axis.Regular(180, 0, 500, name="l0pt", label="l0pt"),
"j0pt" : axis.Regular(180, 0, 500, name="j0pt", label="j0pt"),
"w_lep0_pt" : axis.Regular(180, 0, 300, name="w_lep0_pt", label="Leading W lep pt"),
"w_lep1_pt" : axis.Regular(180, 0, 300, name="w_lep1_pt", label="Subleading W lep pt"),
"z_lep0_pt" : axis.Regular(180, 0, 300, name="z_lep0_pt", label="Leading Z lep pt"),
"z_lep1_pt" : axis.Regular(180, 0, 300, name="z_lep1_pt", label="Subleading Z lep pt"),
"w_lep0_eta" : axis.Regular(180, -3, 3, name="w_lep0_eta", label="Leading W lep eta"),
"w_lep1_eta" : axis.Regular(180, -3, 3, name="w_lep1_eta", label="Subleading W lep eta"),
"z_lep0_eta" : axis.Regular(180, -3, 3, name="z_lep0_eta", label="Leading Z lep eta"),
"z_lep1_eta" : axis.Regular(180, -3, 3, name="z_lep1_eta", label="Subleading Z lep eta"),
"w_lep0_phi" : axis.Regular(180, -4, 4, name="w_lep0_phi", label="Leading W lep phi"),
"w_lep1_phi" : axis.Regular(180, -4, 4, name="w_lep1_phi", label="Subleading W lep phi"),
"z_lep0_phi" : axis.Regular(180, -4, 4, name="z_lep0_phi", label="Leading Z lep phi"),
"z_lep1_phi" : axis.Regular(180, -4, 4, name="z_lep1_phi", label="Subleading Z lep phi"),
"mll_wl0_wl1" : axis.Regular(180, 0, 200, name="mll_wl0_wl1", label="mll(W lep0, W lep1)"),
"mll_zl0_zl1" : axis.Regular(180, 0, 200, name="mll_zl0_zl1", label="mll(Z lep0, Z lep1)"),
"pt_zl0_zl1" : axis.Regular(180, 0, 300, name="pt_zl0_zl1", label="pt(Zl0 + Zl1)"),
"pt_wl0_wl1" : axis.Regular(180, 0, 300, name="pt_wl0_wl1", label="pt(Wl0 + Wl1)"),
"dr_zl0_zl1" : axis.Regular(180, 0, 5, name="dr_zl0_zl1", label="dr(Zl0,Zl1)"),
"dr_wl0_wl1" : axis.Regular(180, 0, 5, name="dr_wl0_wl1", label="dr(Wl0,Wl1)"),
"dr_wleps_zleps" : axis.Regular(180, 0, 5, name="dr_wleps_zleps", label="dr((Wl0+Wl1),(Zl0,Zl1))"),
"absdphi_zl0_zl1" : axis.Regular(180, 0, 4, name="absdphi_zl0_zl1", label="abs dphi(Zl0,Zl1)"),
"absdphi_wl0_wl1" : axis.Regular(180, 0, 4, name="absdphi_wl0_wl1", label="abs dphi(Wl0,Wl1)"),
"absdphi_z_ww" : axis.Regular(180, 0, 4, name="absdphi_z_ww", label="abs dphi((Zl0+Zl1),(Wl0+Wl1+met))"),
"dphi_4l_met" : axis.Regular(180, -4, 4, name="dphi_4l_met", label="dphi((Zl0+Zl1+Wl0+Wl1),met)"),
"dphi_zleps_met" : axis.Regular(180, -4, 4, name="dphi_zleps_met", label="dphi((Zl0+Zl1),met)"),
"dphi_wleps_met" : axis.Regular(180, -4, 4, name="dphi_wleps_met", label="dphi((Wl0+Wl1),met)"),
"absdphi_min_afas" : axis.Regular(180, 0, 4, name="absdphi_min_afas", label="min(abs(delta phi of all pairs))"),
"absdphi_min_afos" : axis.Regular(180, 0, 4, name="absdphi_min_afos", label="min(abs(delta phi of OS pairs))"),
"absdphi_min_sfos" : axis.Regular(180, 0, 4, name="absdphi_min_sfos", label="min(abs(delta phi of SFOS pairs))"),
"mll_min_afas" : axis.Regular(180, 0, 150, name="mll_min_afas", label="min mll of all pairs"),
"mll_min_afos" : axis.Regular(180, 0, 150, name="mll_min_afos", label="min mll of OF pairs"),
"mll_min_sfos" : axis.Regular(180, 0, 150, name="mll_min_sfos", label="min mll of SFOF pairs"),
"mlb_min" : axis.Regular(180, 0, 300, name="mlb_min", label="min mass(b+l)"),
"mlb_max" : axis.Regular(180, 0, 500, name="mlb_max", label="max mass(b+l)"),
"njets" : axis.Regular(8, 0, 8, name="njets", label="Jet multiplicity"),
"nleps" : axis.Regular(5, 0, 5, name="nleps", label="Lep multiplicity"),
"nbtagsl" : axis.Regular(6, 0, 6, name="nbtagsl", label="Loose btag multiplicity"),
"nbtagsm" : axis.Regular(4, 0, 4, name="nbtagsm", label="Medium btag multiplicity"),
"njets_counts" : axis.Regular(30, 0, 30, name="njets_counts", label="Jet multiplicity counts"),
"nleps_counts" : axis.Regular(30, 0, 30, name="nleps_counts", label="Lep multiplicity counts"),
"nbtagsl_counts" : axis.Regular(30, 0, 30, name="nbtagsl_counts", label="Loose btag multiplicity counts"),
"bdt_of_wwz_raw": axis.Regular(180, -3.5, 3.5, name="bdt_of_wwz_raw", label="Raw score bdt_of_wwz"),
"bdt_sf_wwz_raw": axis.Regular(180, -3.5, 3.5, name="bdt_sf_wwz_raw", label="Raw score bdt_sf_wwz"),
"bdt_of_zh_raw" : axis.Regular(180, -3.5, 3.5, name="bdt_of_zh_raw", label="Raw score bdt_of_zh"),
"bdt_sf_zh_raw" : axis.Regular(180, -3.5, 3.5, name="bdt_sf_zh_raw", label="Raw score bdt_sf_zh"),
"bdt_of_wwz": axis.Regular(180, -1, 1, name="bdt_of_wwz", label="Score bdt_of_wwz"),
"bdt_sf_wwz": axis.Regular(180, -1, 1, name="bdt_sf_wwz", label="Score bdt_sf_wwz"),
"bdt_of_zh" : axis.Regular(180, -1, 1, name="bdt_of_zh", label="Score bdt_of_zh"),
"bdt_sf_zh" : axis.Regular(180, -1, 1, name="bdt_sf_zh", label="Score bdt_sf_zh"),
}
# Set the list of hists to fill
if hist_lst is None:
# If the hist list is none, assume we want to fill all hists
self._hist_lst = list(self._dense_axes_dict.keys())
else:
# Otherwise, just fill the specified subset of hists
for hist_to_include in hist_lst:
if hist_to_include not in self._dense_axes_dict.keys():
raise Exception(f"Error: Cannot specify hist \"{hist_to_include}\", it is not defined in the processor.")
self._hist_lst = hist_lst # Which hists to fill
# Set the energy threshold to cut on
self._ecut_threshold = ecut_threshold
# Set the booleans
self._do_errors = do_errors # Whether to calculate and store the w**2 coefficients
self._do_systematics = do_systematics # Whether to process systematic samples
self._split_by_lepton_flavor = split_by_lepton_flavor # Whether to keep track of lepton flavors individually
self._skip_signal_regions = skip_signal_regions # Whether to skip the SR categories
self._skip_control_regions = skip_control_regions # Whether to skip the CR categories
# Main function: run on a given dataset
def process(self, events):
# Loop over samples and fill histos
#hout = {}
#for events in events_dict.values():
# Dataset parameters
dataset = events.metadata["dataset"]
# If we pass the root files instead of events object
#from coffea.nanoevents import NanoEventsFactory
#from coffea.nanoevents import NanoAODSchema
#events = NanoEventsFactory.from_root(
# {fpath: "/Events" for fpath in fpaths},
# schemaclass=NanoAODSchema,
# metadata={"dataset": dataset},
#).events()
isData = self._samples[dataset]["isData"]
histAxisName = self._samples[dataset]["histAxisName"]
year = self._samples[dataset]["year"]
xsec = self._samples[dataset]["xsec"]
sow = self._samples[dataset]["nSumOfWeights"]
# Get up down weights from input dict
if (self._do_systematics and not isData):
lhe_sow = self._samples[dataset]["nSumOfLheWeights"]
# This assumes we have an NLO xsec, so for these systs we will have e.g. xsec_NLO*(N_pass_up/N_gen_up)
# Thus these systs should only affect acceptance and effeciency and shape
# The uncty on xsec comes from NLO and is applied as a rate uncty in the text datacard
if lhe_sow == []:
sow_renormDown = sow
sow_factDown = sow
sow_factUp = sow
sow_renormUp = sow
elif len(lhe_sow) == 9:
sow_renormDown = lhe_sow[1]
sow_factDown = lhe_sow[3]
sow_factUp = lhe_sow[5]
sow_renormUp = lhe_sow[7]
elif len(lhe_sow) == 8:
sow_renormDown = lhe_sow[1]
sow_factDown = lhe_sow[3]
sow_factUp = lhe_sow[4]
sow_renormUp = lhe_sow[6]
else: raise Exception("ERROR: Unknown LHE weights length {len(lhe_sow)}")
else:
sow_renormUp = -1
sow_renormDown = -1
sow_factUp = -1
sow_factDown = -1
datasets = ["SingleMuon", "SingleElectron", "EGamma", "MuonEG", "DoubleMuon", "DoubleElectron", "DoubleEG"]
for d in datasets:
if d in dataset: dataset = dataset.split('_')[0]
# Initialize objects
#met = events.MET
met = events.PuppiMET
ele = events.Electron
mu = events.Muon
tau = events.Tau
jets = events.Jet
# An array of lenght events that is just 1 for each event
# Probably there's a better way to do this, but we use this method elsewhere so I guess why not..
events.nom = ak.ones_like(met.pt)
# Get the lumi mask for data
if year == "2016" or year == "2016APV":
golden_json_path = topcoffea_path("data/goldenJsons/Cert_271036-284044_13TeV_Legacy2016_Collisions16_JSON.txt")
elif year == "2017":
golden_json_path = topcoffea_path("data/goldenJsons/Cert_294927-306462_13TeV_UL2017_Collisions17_GoldenJSON.txt")
elif year == "2018":
golden_json_path = topcoffea_path("data/goldenJsons/Cert_314472-325175_13TeV_Legacy2018_Collisions18_JSON.txt")
else:
raise ValueError(f"Error: Unknown year \"{year}\".")
lumi_mask = LumiMask(golden_json_path)(events.run,events.luminosityBlock)
################### Lepton selection ####################
# Do the object selection for the WWZ eleectrons
ele_presl_mask = os_ec.is_presel_wwz_ele(ele,tight=True)
ele["topmva"] = os_ec.get_topmva_score_ele(events, year)
ele["is_tight_lep_for_wwz"] = ((ele.topmva > get_tc_param("topmva_wp_t_e")) & ele_presl_mask)
# Do the object selection for the WWZ muons
mu_presl_mask = os_ec.is_presel_wwz_mu(mu)
mu["topmva"] = os_ec.get_topmva_score_mu(events, year)
mu["is_tight_lep_for_wwz"] = ((mu.topmva > get_tc_param("topmva_wp_t_m")) & mu_presl_mask)
# Get tight leptons for WWZ selection
ele_wwz_t = ele[ele.is_tight_lep_for_wwz]
mu_wwz_t = mu[mu.is_tight_lep_for_wwz]
# Attach the lepton SFs to the electron and muons collections
cor_ec.AttachElectronSF(ele_wwz_t,year=year)
cor_ec.AttachMuonSF(mu_wwz_t,year=year)
l_wwz_t = ak.with_name(ak.concatenate([ele_wwz_t,mu_wwz_t],axis=1),'PtEtaPhiMCandidate')
l_wwz_t = l_wwz_t[ak.argsort(l_wwz_t.pt, axis=-1,ascending=False)] # Sort by pt
# For WWZ: Compute pair invariant masses
llpairs_wwz = ak.combinations(l_wwz_t, 2, fields=["l0","l1"])
os_pairs_mask = (llpairs_wwz.l0.pdgId*llpairs_wwz.l1.pdgId < 0) # Maks for opposite-sign pairs
sfos_pairs_mask = (llpairs_wwz.l0.pdgId == -llpairs_wwz.l1.pdgId) # Mask for same-flavor-opposite-sign pairs
ll_absdphi_pairs = abs(llpairs_wwz.l0.delta_phi(llpairs_wwz.l1))
ll_mass_pairs = (llpairs_wwz.l0+llpairs_wwz.l1).mass # The mll for each ll pair
absdphi_min_afas = ak.min(ll_absdphi_pairs,axis=-1)
absdphi_min_afos = ak.min(ll_absdphi_pairs[os_pairs_mask],axis=-1)
absdphi_min_sfos = ak.min(ll_absdphi_pairs[sfos_pairs_mask],axis=-1)
mll_min_afas = ak.min(ll_mass_pairs,axis=-1)
mll_min_afos = ak.min(ll_mass_pairs[os_pairs_mask],axis=-1)
mll_min_sfos = ak.min(ll_mass_pairs[sfos_pairs_mask],axis=-1)
events["min_mll_afos"] = mll_min_afos # Attach this one to the event info since we need it for selection
# For WWZ
l_wwz_t_padded = ak.pad_none(l_wwz_t, 4)
l0 = l_wwz_t_padded[:,0]
l1 = l_wwz_t_padded[:,1]
l2 = l_wwz_t_padded[:,2]
l3 = l_wwz_t_padded[:,3]
nleps = ak.num(l_wwz_t)
# Put njets and l_fo_conept_sorted into events and get 4l event selection mask
events["l_wwz_t"] = l_wwz_t
es_ec.add4lmask_wwz(events, year, isData, histAxisName)
######### Normalization and weights ###########
# These weights can go outside of the outside sys loop since they do not depend on pt of mu or jets
# We only calculate these values if not isData
# Note: add() will generally modify up/down weights, so if these are needed for any reason after this point, we should instead pass copies to add()
# Note: Here we will to the weights object the SFs that do not depend on any of the forthcoming loops
weights_obj_base = coffea.analysis_tools.Weights(None,storeIndividual=True)
if not isData:
genw = events["genWeight"]
# If it's an EFT sample, just take SM piece
sm_wgt = 1.0
eft_coeffs = ak.to_numpy(events["EFTfitCoefficients"]) if hasattr(events, "EFTfitCoefficients") else None
if eft_coeffs is not None:
sm_wgt = eft_coeffs[:,0]
# Normalize by (xsec/sow)*genw where genw is 1 for EFT samples
# Note that for theory systs, will need to multiply by sow/sow_wgtUP to get (xsec/sow_wgtUp)*genw and same for Down
lumi = 1000.0*get_tc_param(f"lumi_{year}")
weights_obj_base.add("norm",(xsec/sow)*genw*lumi*sm_wgt)
# Scale weights
cor_tc.AttachPSWeights(events)
cor_tc.AttachScaleWeights(events)
# FSR/ISR weights
# For now only consider variations in the numerator
weights_obj_base.add('ISR', events.nom, events.ISRUp, events.ISRDown)
weights_obj_base.add('FSR', events.nom, events.FSRUp, events.FSRDown)
# Renorm/fact scale
weights_obj_base.add('renorm', events.nom, events.renormUp*(sow/sow_renormUp), events.renormDown*(sow/sow_renormDown))
weights_obj_base.add('fact', events.nom, events.factUp*(sow/sow_factUp), events.factDown*(sow/sow_factDown))
# Misc other experimental SFs and systs
weights_obj_base.add('PreFiring', events.L1PreFiringWeight.Nom, events.L1PreFiringWeight.Up, events.L1PreFiringWeight.Dn)
weights_obj_base.add('PU', cor_tc.GetPUSF((events.Pileup.nTrueInt), year), cor_tc.GetPUSF(events.Pileup.nTrueInt, year, 'up'), cor_tc.GetPUSF(events.Pileup.nTrueInt, year, 'down'))
# Lepton SFs and systs
weights_obj_base.add("lepSF_muon", events.sf_4l_muon, copy.copy(events.sf_4l_hi_muon), copy.copy(events.sf_4l_lo_muon))
weights_obj_base.add("lepSF_elec", events.sf_4l_elec, copy.copy(events.sf_4l_hi_elec), copy.copy(events.sf_4l_lo_elec))
# Set up the list of systematics that are handled via event weight variations
wgt_correction_syst_lst = [
"btagSFlight_correlated", "btagSFbc_correlated", f"btagSFlight_uncorrelated_{year}", f"btagSFbc_uncorrelated_{year}",
"lepSF_elec", "lepSF_muon", "PreFiring", "PU",
"renorm", "fact", "ISR", "FSR",
]
wgt_correction_syst_lst = append_up_down_to_sys_base(wgt_correction_syst_lst)
######### The rest of the processor is inside this loop over systs that affect object kinematics ###########
obj_correction_systs = [] # Will have e.g. jes etc
# If we're doing systematics and this isn't data, we will loop over the obj correction syst lst list
if self._do_systematics and not isData: obj_corr_syst_var_list = ["nominal"] + obj_correction_systs
# Otherwise loop juse once, for nominal
else: obj_corr_syst_var_list = ['nominal']
# Loop over the list of systematic variations (that impact object kinematics) that we've constructed
for obj_corr_syst_var in obj_corr_syst_var_list:
# Make a copy of the base weights object, so that each time through the loop we do not double count systs
# In this loop over systs that impact kinematics, we will add to the weights objects the SFs that depend on the object kinematics
weights_obj_base_for_kinematic_syst = copy.copy(weights_obj_base) # TODO do we need copy here?
#################### Jets ####################
# Clean with dr (though another option is to use jetIdx)
cleanedJets = os_ec.get_cleaned_collection(l_wwz_t,jets)
# Selecting jets and cleaning them
jetptname = "pt_nom" if hasattr(cleanedJets, "pt_nom") else "pt"
cleanedJets["is_good"] = os_tc.is_tight_jet(getattr(cleanedJets, jetptname), cleanedJets.eta, cleanedJets.jetId, pt_cut=20., eta_cut=get_ec_param("wwz_eta_j_cut"), id_cut=get_ec_param("wwz_jet_id_cut"))
goodJets = cleanedJets[cleanedJets.is_good]
# Count jets
njets = ak.num(goodJets)
ht = ak.sum(goodJets.pt,axis=-1)
j0 = goodJets[ak.argmax(goodJets.pt,axis=-1,keepdims=True)]
# Loose DeepJet WP
btagger = "btag" # For deep flavor WPs
#btagger = "btagcsv" # For deep CSV WPs
if year == "2017":
btagwpl = get_tc_param(f"{btagger}_wp_loose_UL17")
btagwpm = get_tc_param(f"{btagger}_wp_medium_UL17")
elif year == "2018":
btagwpl = get_tc_param(f"{btagger}_wp_loose_UL18")
btagwpm = get_tc_param(f"{btagger}_wp_medium_UL18")
elif year=="2016":
btagwpl = get_tc_param(f"{btagger}_wp_loose_UL16")
btagwpm = get_tc_param(f"{btagger}_wp_medium_UL16")
elif year=="2016APV":
btagwpl = get_tc_param(f"{btagger}_wp_loose_UL16APV")
btagwpm = get_tc_param(f"{btagger}_wp_medium_UL16APV")
else:
raise ValueError(f"Error: Unknown year \"{year}\".")
if btagger == "btag":
isBtagJetsLoose = (goodJets.btagDeepFlavB > btagwpl)
isBtagJetsMedium = (goodJets.btagDeepFlavB > btagwpm)
if btagger == "btagcsv":
isBtagJetsLoose = (goodJets.btagDeepB > btagwpl)
isBtagJetsMedium = (goodJets.btagDeepB > btagwpm)
isNotBtagJetsLoose = np.invert(isBtagJetsLoose)
nbtagsl = ak.num(goodJets[isBtagJetsLoose])
isNotBtagJetsMedium = np.invert(isBtagJetsMedium)
nbtagsm = ak.num(goodJets[isBtagJetsMedium])
######### Apply SFs #########
if not isData:
### Evaluate btag weights ###
jets_light = goodJets[goodJets.hadronFlavour==0]
jets_bc = goodJets[goodJets.hadronFlavour>0]
# Workaround to use UL16APV SFs for UL16 for light jets
year_light = year
if year == "2016": year_light = "2016APV"
btag_sf_light = cor_tc.btag_sf_eval(jets_light, "L",year_light,"deepJet_incl","central")
btag_sf_bc = cor_tc.btag_sf_eval(jets_bc, "L",year, "deepJet_comb","central")
btag_eff_light = cor_ec.btag_eff_eval(jets_light,"L",year)
btag_eff_bc = cor_ec.btag_eff_eval(jets_bc,"L",year)
wgt_light = cor_tc.get_method1a_wgt_singlewp(btag_eff_light,btag_sf_light, jets_light.btagDeepFlavB>btagwpl)
wgt_bc = cor_tc.get_method1a_wgt_singlewp(btag_eff_bc, btag_sf_bc, jets_bc.btagDeepFlavB>btagwpl)
wgt_btag_nom = wgt_light*wgt_bc
weights_obj_base_for_kinematic_syst.add("btagSF", wgt_btag_nom)
# Put the btagging up and down weight variations into the weights object
if self._do_systematics:
for btag_sys in ["correlated", "uncorrelated"]:
year_tag = f"_{year}"
if btag_sys == "correlated": year_tag = ""
btag_sf_light_up = cor_tc.btag_sf_eval(jets_light, "L",year_light,"deepJet_incl",f"up_{btag_sys}")
btag_sf_light_down = cor_tc.btag_sf_eval(jets_light, "L",year_light,"deepJet_incl",f"down_{btag_sys}")
btag_sf_bc_up = cor_tc.btag_sf_eval(jets_bc, "L",year, "deepJet_comb",f"up_{btag_sys}")
btag_sf_bc_down = cor_tc.btag_sf_eval(jets_bc, "L",year, "deepJet_comb",f"down_{btag_sys}")
wgt_light_up = cor_tc.get_method1a_wgt_singlewp(btag_eff_light,btag_sf_light_up, jets_light.btagDeepFlavB>btagwpl)
wgt_bc_up = cor_tc.get_method1a_wgt_singlewp(btag_eff_bc, btag_sf_bc_up, jets_bc.btagDeepFlavB>btagwpl)
wgt_light_down = cor_tc.get_method1a_wgt_singlewp(btag_eff_light,btag_sf_light_down, jets_light.btagDeepFlavB>btagwpl)
wgt_bc_down = cor_tc.get_method1a_wgt_singlewp(btag_eff_bc, btag_sf_bc_down, jets_bc.btagDeepFlavB>btagwpl)
# Note, up and down weights scaled by 1/wgt_btag_nom so that don't double count the central btag correction (i.e. don't apply it also in the case of up and down variations)
weights_obj_base_for_kinematic_syst.add(f"btagSFlight_{btag_sys}{year_tag}", events.nom, wgt_light_up*wgt_bc/wgt_btag_nom, wgt_light_down*wgt_bc/wgt_btag_nom)
weights_obj_base_for_kinematic_syst.add(f"btagSFbc_{btag_sys}{year_tag}", events.nom, wgt_light*wgt_bc_up/wgt_btag_nom, wgt_light*wgt_bc_down/wgt_btag_nom)
######### Masks we need for the selection ##########
# Pass trigger mask
pass_trg = es_tc.trg_pass_no_overlap(events,isData,dataset,str(year),dataset_dict=es_ec.dataset_dict,exclude_dict=es_ec.exclude_dict)
pass_trg = (pass_trg & es_ec.trg_matching(events,year))
# b jet masks
bmask_atleast1med_atleast2loose = ((nbtagsm>=1)&(nbtagsl>=2)) # Used for 2lss and 4l
bmask_exactly0loose = (nbtagsl==0) # Used for 4l WWZ SR
bmask_exactly0med = (nbtagsm==0) # Used for 3l CR and 2los Z CR
bmask_exactly1med = (nbtagsm==1) # Used for 3l SR and 2lss CR
bmask_exactly2med = (nbtagsm==2) # Used for CRtt
bmask_atleast2med = (nbtagsm>=2) # Used for 3l SR
bmask_atmost2med = (nbtagsm< 3) # Used to make 2lss mutually exclusive from tttt enriched
bmask_atleast3med = (nbtagsm>=3) # Used for tttt enriched
bmask_atleast1med = (nbtagsm>=1)
bmask_atleast1loose = (nbtagsl>=1)
bmask_atleast2loose = (nbtagsl>=2)
######### WWZ event selection stuff #########
# Get some preliminary things we'll need
es_ec.attach_wwz_preselection_mask(events,l_wwz_t_padded[:,0:4]) # Attach preselection sf and of flags to the events
leps_from_z_candidate_ptordered, leps_not_z_candidate_ptordered = es_ec.get_wwz_candidates(l_wwz_t_padded[:,0:4]) # Get ahold of the leptons from the Z and from the W
w_lep0 = leps_not_z_candidate_ptordered[:,0]
w_lep1 = leps_not_z_candidate_ptordered[:,1]
mll_wl0_wl1 = (w_lep0 + w_lep1).mass
# Make masks for the SF regions
w_candidates_mll_far_from_z = ak.fill_none(abs(mll_wl0_wl1 - get_ec_param("zmass")) > 10.0,False) # Will enforce this for SF in the PackedSelection
ptl4 = (l0+l1+l2+l3).pt
sf_A = ak.fill_none(met.pt >= 120.0,False) # This should never be None, but just keep syntax same as other categories
sf_B = ak.fill_none((met.pt >= 65.0) & (met.pt < 120.0) & (ptl4 >= 70.0),False)
sf_C = ak.fill_none((met.pt >= 65.0) & (met.pt < 120.0) & (ptl4 >= 40.0) & (ptl4 < 70.0),False)
# Make masks for the OF regions
of_1 = ak.fill_none((mll_wl0_wl1 >= 0.0) & (mll_wl0_wl1 < 40.0),False)
of_2 = ak.fill_none((mll_wl0_wl1 >= 40.0) & (mll_wl0_wl1 < 60.0),False)
of_3 = ak.fill_none((mll_wl0_wl1 >= 60.0) & (mll_wl0_wl1 < 100.0),False)
of_4 = ak.fill_none((mll_wl0_wl1 >= 100.0),False)
# Mask for mt2 cut
mt2_val = es_ec.get_mt2(w_lep0,w_lep1,met)
mt2_mask = ak.fill_none(mt2_val>25.0,False)
######### Get variables #########
l0pt = l0.pt
j0pt = ak.flatten(j0.pt) # Flatten to go from [[j0pt],[j0pt],...] -> [j0pt,j0pt,...]
mll_01 = (l0+l1).mass
mllll = (l0+l1+l2+l3).mass
scalarptsum_lep = l0.pt + l1.pt + l2.pt + l3.pt
scalarptsum_lepmet = l0.pt + l1.pt + l2.pt + l3.pt + met.pt
scalarptsum_lepmetjet = l0.pt + l1.pt + l2.pt + l3.pt + met.pt + ak.sum(goodJets.pt,axis=-1)
# Get lep from Z
z_lep0 = leps_from_z_candidate_ptordered[:,0]
z_lep1 = leps_from_z_candidate_ptordered[:,1]
mll_zl0_zl1 = (z_lep0 + z_lep1).mass
pt_zl0_zl1 = (z_lep0 + z_lep1).pt
pt_wl0_wl1 = (w_lep0 + w_lep1).pt
dr_zl0_zl1 = z_lep0.delta_r(z_lep1)
dr_wl0_wl1 = w_lep0.delta_r(w_lep1)
dr_wleps_zleps = (w_lep0 + w_lep1).delta_r(z_lep0 + z_lep1)
absdphi_zl0_zl1 = abs(z_lep0.delta_phi(z_lep1))
absdphi_wl0_wl1 = abs(w_lep0.delta_phi(w_lep1))
absdphi_z_ww = abs((z_lep0 + z_lep1).delta_phi(w_lep0 + w_lep1 + met))
dphi_4l_met = (z_lep0 + z_lep1 + w_lep0 + w_lep1).delta_phi(met)
dphi_wleps_met = (w_lep0 + w_lep1).delta_phi(met)
dphi_zleps_met = (z_lep0 + z_lep1).delta_phi(met)
# lb pairs (i.e. always one lep, one bjet)
bjets = goodJets[isBtagJetsLoose]
#lb_pairs = ak.cartesian({"l":l_wwz_t,"j":bjets})
#mlb_min = ak.min((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1)
#mlb_max = ak.max((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1)
# Get BDT values
bdt_feat_lst = [ "m_ll", "dPhi_4Lep_MET", "dPhi_Zcand_MET", "dPhi_WW_MET", "dR_Wcands", "dR_Zcands", "dR_WW_Z", "MET", "MT2", "Pt4l", "STLepHad", "STLep", "leading_Zcand_pt", "subleading_Zcand_pt", "leading_Wcand_pt", "subleading_Wcand_pt"]
bdt_var_dict = {
"m_ll" : ak.fill_none(mll_wl0_wl1,-9999),
"dPhi_4Lep_MET" : ak.fill_none(dphi_4l_met,-9999),
"dPhi_Zcand_MET" : ak.fill_none(dphi_zleps_met,-9999),
"dPhi_WW_MET" : ak.fill_none(dphi_wleps_met,-9999),
"dR_Wcands" : ak.fill_none(dr_wl0_wl1,-9999),
"dR_Zcands" : ak.fill_none(dr_zl0_zl1,-9999),
"dR_WW_Z" : ak.fill_none(dr_wleps_zleps,-9999),
"MET" : ak.fill_none(met.pt,-9999),
"MT2" : ak.fill_none(mt2_val,-9999),
"Pt4l" : ak.fill_none(ptl4,-9999),
"STLepHad" : ak.fill_none(scalarptsum_lepmet,-9999),
"STLep" : ak.fill_none(scalarptsum_lepmetjet,-9999),
"leading_Zcand_pt" : ak.fill_none(z_lep0.pt,-9999),
"subleading_Zcand_pt": ak.fill_none(z_lep1.pt,-9999),
"leading_Wcand_pt" : ak.fill_none(w_lep0.pt,-9999),
"subleading_Wcand_pt": ak.fill_none(w_lep1.pt,-9999),
}
bdt_of_wwz_raw = os_ec.xgb_eval_wrapper(bdt_feat_lst,bdt_var_dict,ewkcoffea_path("data/wwz_zh_bdt/of_WWZ.json"))
bdt_sf_wwz_raw = os_ec.xgb_eval_wrapper(bdt_feat_lst,bdt_var_dict,ewkcoffea_path("data/wwz_zh_bdt/sf_WWZ.json"))
bdt_of_zh_raw = os_ec.xgb_eval_wrapper(bdt_feat_lst,bdt_var_dict,ewkcoffea_path("data/wwz_zh_bdt/of_ZH.json"))
bdt_sf_zh_raw = os_ec.xgb_eval_wrapper(bdt_feat_lst,bdt_var_dict,ewkcoffea_path("data/wwz_zh_bdt/sf_ZH.json"))
# Match TMVA's scaling https://root.cern.ch/doc/v606/MethodBDT_8cxx_source.html
bdt_of_wwz = (2.0*((1.0+math.e**(-2*bdt_of_wwz_raw))**(-1))) - 1.0
bdt_sf_wwz = (2.0*((1.0+math.e**(-2*bdt_sf_wwz_raw))**(-1))) - 1.0
bdt_of_zh = (2.0*((1.0+math.e**(-2*bdt_of_zh_raw))**(-1))) - 1.0
bdt_sf_zh = (2.0*((1.0+math.e**(-2*bdt_sf_zh_raw))**(-1))) - 1.0
### BDT SRs ###
# SF BDT SRs
sf_wwz_sr1 = ( (bdt_sf_wwz > 0.9) & (bdt_sf_zh > 0.8))
sf_wwz_sr2 = ( (bdt_sf_wwz > 0.9) & (bdt_sf_zh > -0.6) & (bdt_sf_zh < 0.8))
sf_zh_sr1 = ( (bdt_sf_wwz < 0.9) & (bdt_sf_wwz > 0.7) & (bdt_sf_zh > 0.85))
sf_zh_sr2 = ( (bdt_sf_wwz < 0.7) & (bdt_sf_wwz > 0.6) & (bdt_sf_zh > 0.85))
sf_any = ( sf_wwz_sr1 | sf_wwz_sr2 | sf_zh_sr1 | sf_zh_sr2)
sf_wwz_sr3 = ( ~sf_any & ((bdt_sf_zh > 0.5) & (bdt_sf_wwz > 0.35)))
sf_wwz_sr4 = ( ~(sf_any | sf_wwz_sr3) & ( (bdt_sf_zh > 0.85) & (bdt_sf_wwz > -0.5)))
sf_zh_sr3 = ( ~(sf_any | sf_wwz_sr3 | sf_wwz_sr4) & ( bdt_sf_wwz > 0.8 ) )
# OF BDT SRs
of_wwz_sr1 = ( (bdt_of_wwz > 0.7) & (bdt_of_zh < -0.3) )
of_wwz_sr2 = ( (bdt_of_wwz < 0.7) & (bdt_of_wwz > 0.4) & (bdt_of_zh < -0.6) )
of_zh_sr1 = ( (bdt_of_wwz > 0.5) & (bdt_of_zh > 0.7) )
of_zh_sr2 = ( (bdt_of_wwz < 0.5) & (bdt_of_wwz > -0.2) & (bdt_of_zh > 0.7) )
of_any = ( of_wwz_sr1 | of_wwz_sr2 | of_zh_sr1 | of_zh_sr2 )
of_wwz_sr3 = ( ~of_any & (bdt_of_wwz > 0.0) & (bdt_of_zh < (0.8*(bdt_of_wwz-1.))) )
of_wwz_sr4 = ( (~of_any & ~of_wwz_sr3) & (bdt_of_wwz > 0.0) )
of_zh_sr3 = ( (~of_any & ~of_wwz_sr3 & ~of_wwz_sr4) & (bdt_of_zh > 0.5) )
of_zh_sr4 = ( (~of_any & ~of_wwz_sr3 & ~of_wwz_sr4 & ~of_zh_sr3) & (bdt_of_zh > 0.0) & (bdt_of_wwz > -0.5) )
######### Store boolean masks with PackedSelection ##########
selections = PackedSelection(dtype='uint64')
# Lumi mask (for data)
selections.add("is_good_lumi",lumi_mask)
zeroj = (njets==0)
# For WWZ selection
selections.add("sr_4l_sf_A", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_A))
selections.add("sr_4l_sf_B", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_B))
selections.add("sr_4l_sf_C", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_C))
selections.add("sr_4l_of_1", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_1 & mt2_mask))
selections.add("sr_4l_of_2", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_2 & mt2_mask))
selections.add("sr_4l_of_3", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_3 & mt2_mask))
selections.add("sr_4l_of_4", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_4))
selections.add("all_events", (events.is4lWWZ | (~events.is4lWWZ))) # All events.. this logic is a bit roundabout to just get an array of True
selections.add("4l_presel", (events.is4lWWZ)) # This matches the VVV looper selection (object selection and event selection)
selections.add("sr_4l_sf_presel", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & (met.pt > 65.0)))
selections.add("sr_4l_of_presel", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of))
# For BDT SRs
selections.add("sr_4l_bdt_sf_wwz_sr1", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & sf_wwz_sr1))
selections.add("sr_4l_bdt_sf_wwz_sr2", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & sf_wwz_sr2))
selections.add("sr_4l_bdt_sf_wwz_sr3", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & sf_wwz_sr3))
selections.add("sr_4l_bdt_sf_wwz_sr4", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & sf_wwz_sr4))
selections.add("sr_4l_bdt_sf_zh_sr1", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & sf_zh_sr1))
selections.add("sr_4l_bdt_sf_zh_sr2", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & sf_zh_sr2))
selections.add("sr_4l_bdt_sf_zh_sr3", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & sf_zh_sr3))
selections.add("sr_4l_bdt_of_wwz_sr1", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_wwz_sr1))
selections.add("sr_4l_bdt_of_wwz_sr2", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_wwz_sr2))
selections.add("sr_4l_bdt_of_wwz_sr3", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_wwz_sr3))
selections.add("sr_4l_bdt_of_wwz_sr4", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_wwz_sr4))
selections.add("sr_4l_bdt_of_zh_sr1", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_zh_sr1))
selections.add("sr_4l_bdt_of_zh_sr2", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_zh_sr2))
selections.add("sr_4l_bdt_of_zh_sr3", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_zh_sr3))
selections.add("sr_4l_bdt_of_zh_sr4", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_zh_sr4))
# CRs
ww_ee = ((abs(w_lep0.pdgId) == 11) & (abs(w_lep1.pdgId) == 11))
ww_mm = ((abs(w_lep0.pdgId) == 13) & (abs(w_lep1.pdgId) == 13))
ww_em = ((abs(w_lep0.pdgId) == 11) & (abs(w_lep1.pdgId) == 13))
ww_me = ((abs(w_lep0.pdgId) == 13) & (abs(w_lep1.pdgId) == 11))
selections.add("cr_4l_btag_of", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_of))
selections.add("cr_4l_btag_sf", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf))
selections.add("cr_4l_btag_sf_offZ", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf & w_candidates_mll_far_from_z))
selections.add("cr_4l_btag_sf_offZ_met80", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & (met.pt > 80.0)))
selections.add("cr_4l_sf", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & (~w_candidates_mll_far_from_z)))
bdt_sr_names = [
"sr_4l_bdt_sf_wwz_sr1",
"sr_4l_bdt_sf_wwz_sr2",
"sr_4l_bdt_sf_wwz_sr3",
"sr_4l_bdt_sf_wwz_sr4",
"sr_4l_bdt_sf_zh_sr1",
"sr_4l_bdt_sf_zh_sr2",
"sr_4l_bdt_sf_zh_sr3",
"sr_4l_bdt_of_wwz_sr1",
"sr_4l_bdt_of_wwz_sr2",
"sr_4l_bdt_of_wwz_sr3",
"sr_4l_bdt_of_wwz_sr4",
"sr_4l_bdt_of_zh_sr1",
"sr_4l_bdt_of_zh_sr2",
"sr_4l_bdt_of_zh_sr3",
"sr_4l_bdt_of_zh_sr4",
]
cat_dict = {
"lep_chan_lst" : [
"sr_4l_sf_A","sr_4l_sf_B","sr_4l_sf_C","sr_4l_of_1","sr_4l_of_2","sr_4l_of_3","sr_4l_of_4",
"sr_4l_sf_presel", "sr_4l_of_presel",
"all_events","4l_presel",
"cr_4l_btag_of","cr_4l_sf", "cr_4l_btag_sf", "cr_4l_btag_sf_offZ", "cr_4l_btag_sf_offZ_met80",
] + bdt_sr_names
}
######### Fill histos #########
dense_variables_dict = {
"mt2" : mt2_val,
"met" : met.pt,
"metphi" : met.phi,
"ptl4" : ptl4,
"scalarptsum_lep" : scalarptsum_lep,
"scalarptsum_lepmet" : scalarptsum_lepmet,
"scalarptsum_lepmetjet" : scalarptsum_lepmetjet,
"mll_01" : mll_01,
"mllll" : mllll,
"l0pt" : l0pt,
"j0pt" : j0pt,
"z_lep0_pt" : z_lep0.pt,
"z_lep1_pt" : z_lep1.pt,
"w_lep0_pt" : w_lep0.pt,
"w_lep1_pt" : w_lep1.pt,
"z_lep0_eta" : z_lep0.eta,
"z_lep1_eta" : z_lep1.eta,
"w_lep0_eta" : w_lep0.eta,
"w_lep1_eta" : w_lep1.eta,
"z_lep0_phi" : z_lep0.phi,
"z_lep1_phi" : z_lep1.phi,
"w_lep0_phi" : w_lep0.phi,
"w_lep1_phi" : w_lep1.phi,
"mll_wl0_wl1" : mll_wl0_wl1,
"mll_zl0_zl1" : mll_zl0_zl1,
"pt_zl0_zl1" : pt_zl0_zl1,
"pt_wl0_wl1" : pt_wl0_wl1,
"dr_zl0_zl1" : dr_zl0_zl1,
"dr_wl0_wl1" : dr_wl0_wl1,
"dr_wleps_zleps" : dr_wleps_zleps,
"absdphi_zl0_zl1" : absdphi_zl0_zl1,
"absdphi_wl0_wl1" : absdphi_wl0_wl1,
"absdphi_z_ww" : absdphi_z_ww,
"dphi_4l_met" : dphi_4l_met,
"dphi_zleps_met" : dphi_zleps_met,
"dphi_wleps_met" : dphi_wleps_met,
"nleps" : nleps,
"njets" : njets,
"nbtagsl" : nbtagsl,
"nleps_counts" : nleps,
"njets_counts" : njets,
"nbtagsl_counts" : nbtagsl,
"absdphi_min_afas" : absdphi_min_afas,
"absdphi_min_afos" : absdphi_min_afos,
"absdphi_min_sfos" : absdphi_min_sfos,
"mll_min_afas" : mll_min_afas,
"mll_min_afos" : mll_min_afos,
"mll_min_sfos" : mll_min_sfos,
#"mlb_min" : mlb_min,
#"mlb_max" : mlb_max,
"bdt_of_wwz_raw": bdt_of_wwz_raw,
"bdt_sf_wwz_raw": bdt_sf_wwz_raw,
"bdt_of_zh_raw" : bdt_of_zh_raw,
"bdt_sf_zh_raw" : bdt_sf_zh_raw,
"bdt_of_wwz": bdt_of_wwz,
"bdt_sf_wwz": bdt_sf_wwz,
"bdt_of_zh" : bdt_of_zh,
"bdt_sf_zh" : bdt_sf_zh,
}
# List the hists that are only defined for some categories
analysis_cats = ["sr_4l_sf_A","sr_4l_sf_B","sr_4l_sf_C","sr_4l_of_1","sr_4l_of_2","sr_4l_of_3","sr_4l_of_4"] + bdt_sr_names
exclude_var_dict = {
"mt2" : ["all_events"],
"ptl4" : ["all_events"],
"j0pt" : ["all_events", "4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats,
"l0pt" : ["all_events"],
"mll_01" : ["all_events"],
"mllll" : ["all_events"],
"scalarptsum_lep" : ["all_events"],
"scalarptsum_lepmet" : ["all_events"],
"scalarptsum_lepmetjet" : ["all_events"],
"w_lep0_pt" : ["all_events"],
"w_lep1_pt" : ["all_events"],
"z_lep0_pt" : ["all_events"],
"z_lep1_pt" : ["all_events"],
"w_lep0_eta" : ["all_events"],
"w_lep1_eta" : ["all_events"],
"z_lep0_eta" : ["all_events"],
"z_lep1_eta" : ["all_events"],
"w_lep0_phi" : ["all_events"],
"w_lep1_phi" : ["all_events"],
"z_lep0_phi" : ["all_events"],
"z_lep1_phi" : ["all_events"],
"mll_wl0_wl1" : ["all_events"],
"mll_zl0_zl1" : ["all_events"],
"pt_zl0_zl1" : ["all_events"],
"pt_wl0_wl1" : ["all_events"],
"dr_zl0_zl1" : ["all_events"],
"dr_wl0_wl1" : ["all_events"],
"dr_wleps_zleps" : ["all_events"],
"absdphi_zl0_zl1" : ["all_events"],
"absdphi_wl0_wl1" : ["all_events"],
"absdphi_z_ww" : ["all_events"],
"dphi_4l_met" : ["all_events"],
"dphi_zleps_met" : ["all_events"],
"dphi_wleps_met" : ["all_events"],
"absdphi_min_afas" : ["all_events"],
"absdphi_min_afos" : ["all_events"],
"absdphi_min_sfos" : ["all_events"],
"mll_min_afas" : ["all_events"],
"mll_min_afos" : ["all_events"],
"mll_min_sfos" : ["all_events"],
"mlb_min" : ["all_events","4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats,
"mlb_max" : ["all_events","4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats,
"bdt_of_wwz_raw": ["all_events"],
"bdt_sf_wwz_raw": ["all_events"],
"bdt_of_zh_raw" : ["all_events"],
"bdt_sf_zh_raw" : ["all_events"],
"bdt_of_wwz": ["all_events"],
"bdt_sf_wwz": ["all_events"],
"bdt_of_zh" : ["all_events"],
"bdt_sf_zh" : ["all_events"],
}
# Set up the list of weight fluctuations to loop over
# For now the syst do not depend on the category, so we can figure this out outside of the filling loop
wgt_var_lst = ["nominal"]
if self._do_systematics:
if not isData:
if (obj_corr_syst_var != "nominal"):
# In this case, we are dealing with systs that change the kinematics of the objs (e.g. JES)
# So we don't want to loop over up/down weight variations here
wgt_var_lst = [obj_corr_syst_var]
else:
# Otherwise we want to loop over the up/down weight variations
wgt_var_lst = wgt_var_lst + wgt_correction_syst_lst
# Loop over the hists we want to fill
hout = {} # This is what we'll eventually return
masked_val_cache = {} # So we don't need to build the same mask multiple times
masked_weights_cache = {} # So we don't need to build the same mask multiple times
for dense_axis_name, dense_axis_vals in dense_variables_dict.items():
if dense_axis_name not in self._hist_lst:
print(f"Skipping \"{dense_axis_name}\", it is not in the list of hists to include.")
continue
#print("\ndense_axis_name,vals",dense_axis_name)
#print("dense_axis_name,vals",dense_axis_vals)
# Create the hist for this dense axis variable
hout[dense_axis_name] = hda.Hist(
hist.axis.StrCategory([], growth=True, name="process", label="process"),
hist.axis.StrCategory([], growth=True, name="category", label="category"),
hist.axis.StrCategory([], growth=True, name="systematic", label="systematic"),
self._dense_axes_dict[dense_axis_name],
storage="weight", # Keeps track of sumw2
name="Counts",
)
# Loop over weight fluctuations
for wgt_fluct in wgt_var_lst:
# Get the appropriate weight fluctuation
if (wgt_fluct == "nominal") or (wgt_fluct in obj_corr_syst_var_list):
# In the case of "nominal", no weight systematic variation is used
weight = weights_obj_base_for_kinematic_syst.weight(None)
else:
# Otherwise get the weight from the Weights object
weight = weights_obj_base_for_kinematic_syst.weight(wgt_fluct)
# Loop over categories
for sr_cat in cat_dict["lep_chan_lst"]:
# Skip filling if this variable is not relevant for this selection
if (dense_axis_name in exclude_var_dict) and (sr_cat in exclude_var_dict[dense_axis_name]): continue
# Make the cuts mask
cuts_lst = [sr_cat]
if isData: cuts_lst.append("is_good_lumi") # Apply golden json requirements if this is data
all_cuts_mask = selections.all(*cuts_lst)
# Used cached masked dense axis variable vals if we've already computed it
var_key = tuple(cuts_lst + [dense_axis_name])
if var_key not in masked_val_cache:
masked_val_cache[var_key] = dense_axis_vals[all_cuts_mask]
masked_vals = masked_val_cache[var_key]
# Used cached masked weights if we've already computed it
if isData or dense_axis_name.endswith("_counts"):
# Special case for the *_counts histograms, these get filled with unit weights (like data)
# So need a different weights_key for this special case
weight = events.nom
weights_key = tuple(cuts_lst + [wgt_fluct, "weights_counts"])
else:
weights_key = tuple(cuts_lst + [wgt_fluct, "weights_norm"])
if weights_key not in masked_weights_cache:
masked_weights_cache[weights_key] = weight[all_cuts_mask]
masked_weight = masked_weights_cache[weights_key]
#run = events.run[all_cuts_mask]
#luminosityBlock = events.luminosityBlock[all_cuts_mask]
#event = events.event[all_cuts_mask]
#w = weights[all_cuts_mask]
#if dense_axis_name == "njets":
# print("\nSTARTPRINT")
# for i,j in enumerate(w):
# out_str = f"PRINTTAG {i} {dense_axis_name} {year} {sr_cat} {event[i]} {run[i]} {luminosityBlock[i]} {w[i]}"
# print(out_str,file=sys.stderr,flush=True)
# print("ENDPRINT\n")
#print("\ndense_axis_name",dense_axis_name)
#print("sr_cat",sr_cat)
#print("dense_axis_vals[all_cuts_mask]",dense_axis_vals[all_cuts_mask])
#print("end")
# Fill the histos
axes_fill_info_dict = {
dense_axis_name : masked_vals,
"weight" : masked_weight,
"process" : histAxisName,
"category" : sr_cat,
"systematic" : wgt_fluct,
}
hout[dense_axis_name].fill(**axes_fill_info_dict)
return hout
def postprocess(self, accumulator):
return accumulator